Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma

Construction of a disulfidptosis-related glycolysis gene risk model to predict the prognosis and immune infiltration analysis of gastric adenocarcinoma

BACKGROUND

The pattern of cell death known as disulfidptosis was recently discovered. Disulfidptosis, which may affect the growth of tumor cells, represents a potential new approach to treating tumors. Glycolysis affects tumor proliferation, invasion, chemotherapy resistance, the tumor microenvironment (TME), and immune evasion. However, the efficacy and therapeutic significance of disulfidptosis-related glycolysis genes (DRGGs) in stomach adenocarcinoma (STAD) remain uncertain.

METHODS

STAD clinical data and RNA sequencing data were downloaded from the TCGA database. DRGGs were screened using Cox regression and Lasso regression analysis to construct a prognostic risk model. The accuracy of the model was verified using survival studies, receiver operating characteristic (ROC) curves, column plots, and calibration curves. Additionally, our study investigated the relationships between the risk scores and immune cell infiltration, tumor mutational burden (TMB), and anticancer drug sensitivity.

RESULTS

We have successfully developed a prognosis risk model with 4 DRGGs (NT5E, ALG1, ANKZF1, and VCAN). The model showed excellent performance in predicting the overall survival of STAD patients. The DRGGs prognostic model significantly correlated with the TME, immune infiltrating cells, and treatment sensitivity.

CONCLUSIONS

The risk model developed in this work has significant clinical value in predicting the impact of immunotherapy in STAD patients and assisting in the choice of chemotherapeutic medicines. It can correctly estimate the prognosis of STAD patients.

Targeting anticancer immunity in melanoma tumour microenvironment: unleashing the potential of adjuvants, drugs, and phytochemicals

Melanoma poses a challenge in oncology because of its aggressive nature and limited treatment modalities. The tumour microenvironment (TME) in melanoma contains unique properties such as an immunosuppressive and high-density environment, unusual vasculature, and a high number of stromal and immunosuppressive cells. In recent years, numerous experiments have focused on boosting the immune system to effectively remove malignant cells. Adjuvants, consisting of phytochemicals, toll-like receptor (TLR) agonists, and cytokines, have shown encouraging results in triggering antitumor immunity and augmenting the therapeutic effectiveness of anticancer therapy. These adjuvants can stimulate the maturation of dendritic cells (DCs) and infiltration of cytotoxic CD8+ T lymphocytes (CTLs). Furthermore, nanocarriers can help to deliver immunomodulators and antigens directly to the tumour stroma, thereby improving their efficacy against malignant cells. The remodelling of melanoma TME utilising phytochemicals, agonists, and other adjuvants can be combined with current modalities for improving therapy outcomes. This review article explores the potential of adjuvants, drugs, and their nanoformulations in enhancing the anticancer potency of macrophages, CTLs, and natural killer (NK) cells. Additionally, the capacity of these agents to repress the function of immunosuppressive components of melanoma TME, such as immunosuppressive subsets of macrophages, stromal and myeloid cells will be discussed.

SIRT2 as a Potential Biomarker in Lung Adenocarcinoma: Implications for Immune Infiltration

SIRT2 play important roles in cell cycle and cellular metabolism in the development of non-small cell lung cancer (NSCLC), and SIRT2 exhibits its therapeutic effect on NSCLC tumors with high expression of SIRT2. Nevertheless, the clinical relevance of SIRT2 in lung adenocarcinoma (LUAD), particularly its impact on tumor growth and prognostic implications, remains obscure. This investigation entailed a comprehensive analysis of SIRT2 mRNA and protein expression levels in diverse tumor and corresponding healthy tissues, utilizing databases such as TIMER 2.0, UALCAN, and HPA. Prognostic correlations of SIRT2 expression in LUAD patients, stratified by distinct clinicopathological characteristics, were evaluated using the KM Plotter database. Additionally, the TCGA and TIMER 2.0 databases were employed to assess the relationship between SIRT2 and immune infiltration, as well as to calculate immunity, stromal, and estimation scores, thus elucidating the role of SIRT2 in modulating tumor immunotherapy responses. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to elucidate SIRT2's biological functions in pan-cancer cells. Our findings revealed a marked reduction in both mRNA and protein levels of SIRT2 in LUAD tumors relative to healthy tissue. Survival analysis indicated that diminished SIRT2 expression correlates with adverse prognostic outcomes in LUAD. Furthermore, SIRT2 expression demonstrated a significant association with various clinicopathologic attributes of LUAD patients, influencing survival outcomes across different clinicopathologic states. Functional enrichment analyses highlighted SIRT2's involvement in cell cycle regulation and immune response. Notably, SIRT2 exhibited a positive correlation with immune cell infiltration, including natural killer (NK) cells, macrophages, and dendritic cells (DCs). In summary, SIRT2 was a potential prognostic biomarker for LUAD and and a new immunotherapy target.

Tumorigenic role of Pak4 in ovarian cancer and its correlation with immune infiltration

BACKGROUND

Ovarian cancer is the most common cause of gynecological cancer death. Pak4 has been proved to be tumorigenic in many types of cancers, but its role in ovarian cancer is still not clarified.

METHODS

In this study, we used immunohistochemistry to investigate into Pak4 expression in different histological types of ovarian cancer. TIMER, TISCH2, GEPIA, ualcan, KM plotter, GSCA and GeneMANIA were used to identify the prognostic roles and gene regulation networks of Pak4 in ovarian cancer. Immune infiltration levels were investigated using TIMER database.

RESULTS

Pak4 was highly expressed in ovarian cancers, regardless of different FIGO stages and histological grades. Single cell sequencing database proved that Pak4 was highly expressed in malignant ovarian cancer cells. Pak4 level was significantly correlated with different histological types of ovarian cancer. Pak4 expression was negatively connected with OS and PFS of ovarian cancer patients. Functions of Pak4 and its interacted genes were mainly involved in protein serine/threonine kinase activity, regulation of actin filament-based process and regulation of cytoskeleton organization. Pak4 level was negatively correlated with immune biomarkers of B cell infiltration (p = 2.39e-05), CD8 + T cell infiltration (p = 1.51e-04), neutrophil (p = 1.74e-06) and dendritic cell (p = 4.41e-08). Close correlation was found between Pak4 expression and T cell exhaustion (p < 0.05).

CONCLUSIONS

Our results demonstrated the expression level, gene interaction networks and immune infiltration levels of Pak4 in ovarian cancer. And the results revealed role of Pak4 in tumorigenesis and the possibility to be a potential immunotherapeutic target.

Death associated protein kinase 1 predicts the prognosis and the immunotherapy response of various cancers

BACKGROUND

Death Associated Protein Kinase 1 (DAPK1) is a calcium/calmodulin-dependent serine/threonine kinase, which has been reported to be a tumor suppressor with unbalanced expression in various tissues. However, its function in tumor immunotherapy is still unclear.

METHODS

The online GEPIA2 database was used to support TCGA results. We explored the DAPK1 pan-cancer genomic alteration analysis using the cBioPortal web tool. The Human Protein Atlas (HPA) was employed to mine DAPK1 protein information. We verified the expression of DAPK1 in lung adenocarcinoma samples using RT-qPCR. Subsequently, the relationship between the expression of DAPK1 and the clinical stage was analyzed. We used TIMER2.0 as the primary platform for studying DAPK1-related immune cell infiltration. Associations between DAPK1 and immunotherapy biomarkers were analyzed using Spearman correlation analysis. TMB and MSI expression was also examined. Finally, we used Kaplan-Meier Plots to evaluate the relationship between DAPK1 expression and the efficacy of immunotherapy.

RESULTS

DAPK1 is aberrantly expressed in most cancer types and has prognostic power in various cancers. Gene mutation was the most common DAPK1 alteration across pan-cancers. The DAPK1 protein was mainly localized to tumor cell centrosomes. DAPK1 was also significantly associated with immune-activated hallmarks, immune cell infiltration, and the expression of immunomodulators. Notably, DAPK1 can also significantly predict responses to anti-PD1 and anti-CTLA-4 therapy in cancer patients.

CONCLUSIONS

Our findings suggest that DAPK1 may not only be an effective prognostic factor in cancer patients but may also function as a promising predictive immunotherapy biomarker for cancer patients treated with immune checkpoint inhibitors.

Unveiling research trends in the prognosis of osteosarcoma: A bibliometric analysis from 2000 to 2022

Background

Osteosarcoma (OSA) is the most prevalent form of malignant bone tumor in children and adolescents, producing osteoid and immature bone. Numerous high quality studies have been published in the OSA field, however, no bibliometric study related to this area has been reported thus far. Therefore, the present study retrieved the published data from 2000 to 2022 to reveal the dynamics, development trends, hotspots and future directions of the OSA.

Methods

Publications regard to osteogenic sarcoma and prognosis were searched in the core collection on Web of Science database. The retrieved publications were analyzed by publication years, journals, categories, countries, citations, institutions, authors, keywords and clusters using the two widely available bibliometric visualization tools, VOS viewer (Version 1.6.16), Citespace (Version 6.2. R1).

Results

A total of 6260 publications related to the current topic were retrieved and analyzed, revealing exponential increase in the number of publications with an improvement in the citations on the OSA over time, in which China and the USA are the most productive nations. Shanghai Jiao Tong University, University of Texas System and Harvard University are prolific institutions, having highest collaboration network. Oncology Letters and Journal of Clinical Oncology are the most productive and the most cited journals respectively. The Wang Y is a prominent author and articles published by Bacci G had the highest number of citations indicating their significant impact in the field. According to keywords analysis, osteosarcoma, expression and metastasis were the most apparent keywords whereas the current research hotspots are biomarker, tumor microenvironment, immunotherapy and DNA methylation.

Conclusion

Our findings offer valuable information for researchers to understand the current research status and the necessity of future research to mitigate the mortality of the OS patients.

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

BACKGROUND

Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.

PURPOSE

The purpose of this study was to explore the potential application value of pathomics signatures.

METHODS

Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links.

RESULTS

The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

Prognostic risk of immune-associated signature in the microenvironment of brain gliomas

Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes.

Biological role and diagnostic utility of ribosomal protein L23a pseudogene 53 in cutaneous melanoma

Background

Skin melanoma is one of the deadliest types of skin cancer and develops from melanocytes. The genetic aberrations in protein-coding genes are well characterized, but little is known about changes in non-coding RNAs (ncRNAs) such as pseudogenes. Ribosomal protein pseudogenes (RPPs) have been described as the largest group of pseudogenes which are dispersed in the human genome.

Materials and methids

We looked deeply at the role of one of them, ribosomal protein L23a pseudogene 53 (RPL23AP53), and its potential diagnostic use. The expression level of RPL23AP53 was profiled in melanoma cell lines using real time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and analyzed based on the Cancer Genome Atlas (TCGA) data depending on BRAF status and clinicopathological parameters. Cellular phenotype, which was associated with RPL23AP53 levels, was described based on the REACTOME pathway browser, Gene Set Enrichment Analysis (GSEA) analysis as well as Immune and ESTIMATE Scores.

Results

We indicted in vitro changes in RPL23AP53 level depending on a cell line, and based on in silico analysis of TCGA samples demonstrated significant differences in RPL23AP53 expression between primary and metastatic melanoma, as well as correlation between RPL23AP53 and overall survival. No differences depending on BRAF status were observed. RPL23AP53 is associated with several signaling pathways and cellular processes.

Conclusions

This study showed that patients with higher expression of RPL23AP53 displayed changed infiltration of lymphocytes, macrophages, and neutrophils compared to groups with lower expression of RPL23AP53. RPL23AP53 pseudogene is differently expressed in melanoma compared with normal tissue and its expression is associated with cellular proliferation. Thus, it may be considered as an indicator of patients' survival and a marker for the immune profile assessment.

Infiltration of LPAR5+ macrophages in osteosarcoma tumor microenvironment predicts better outcomes

Introduction

Tumor microenvironment (TME) has been shown to be extensively involved in tumor development. However, the dynamic change of TME components and their effects are still unclear. Here, we attempted to identify TME-related genes that could help predict survival and may be potential therapeutic targets.

Methods

Data was collected from UCSC Xena and GEO database. ESTIMATE and CIBERSORT algorithms were applied to estimate the components and the proportions of TIICs in TME. We analyzed the gene expression differences of immune components and stromal components, respectively, and finally got the overlapped DEGs. Through protein-protein interaction (PPI) network and univariate Cox regression analysis based on shared DEGs, we screened out and validated the TME-related genes. Focusing on this gene, we analyzed the expression and prognostic value of this gene, and investigated its relationship with immune cells by correlation analysis, single cell analysis, immunohistochemistry and immunofluorescence analysis.

Results

Through a series analysis, we found that the proportion of immune and stromal components was an important prognostic factor, and screened out a key gene, LPAR5, which was highly correlated with prognosis and metastasis. And the expression of LPAR5 was positively correlated with immune cells, especially macrophages, indicating LPAR5⁺ macrophages played an important role in tumor microenvironment of osteosarcoma. Meanwhile, the genes in LPAR5 high expression group were enriched in immune-related activities and pathways, and differentially expressed genes between LPAR5⁺ macrophages and LPAR5^- macrophages were enriched in the biological processes associated with phagocytosis and antigen presentation. What' more, we found that LPAR5 was mainly expressed in TME, and high LPAR5 expression predicting a better prognosis.

Conclusion

We identified a TME-related gene, LPAR5, which is a promising indicator for TME remodeling in osteosarcoma. Particularly, LPAR5⁺ macrophages might have great potential to be a prognostic factor and therapeutic target for osteosarcoma.

C1QA, C1QB, and GZMB are novel prognostic biomarkers of skin cutaneous melanoma relating tumor microenvironment

Skin cutaneous melanoma (SKCM) is the most lethal form of skin cancers owing to high invasiveness and high metastatic potential. Tumor microenvironment (TME) provides powerful evidences for discerning SKCM, raising the prospect to identify biomarkers of SKCM. Based on the transcriptome profiles of patients with SKCM and the corresponding clinical information from The Cancer Genome Atlas (TCGA), we used ESTIMATE algorithm to calculate ImmuneScore and StromalScore and identified the TME-Related differentially expressed genes (DEGs), than the intersected TME-Related DEGs were used for subsequent functional enrichment analysis. Protein-protein interaction (PPI) analysis was used to identify the functionality-related DEGs and univariate Cox regression analysis was used to identify the survival-related DEGs. Furthermore, SKCM-related DEGs were identified based on two Gene Expression Omnibus (GEO) datasets. Finally, we intersected functionality-related DEGs, survival-related DEGs, and SKCM-related DEGs, ascertaining that six DEGs (CCL4, CXCL10, CCL5, GZMB, C1QA, and C1QB) function as core TME-related genes (CTRGs). Significant differences of GZMB, C1QA, and C1QB expressions were found in gender and clinicopathologic staging of SKCM. High levels of GZMB, C1QA, and C1QB expressions were associated with favorable prognosis. Gene set enrichment analysis (GSEA) showed that cell-cell interaction, cell behavior, and intracellular signaling transduction may be mainly involved in both C1QA, C1QB and GZMB expressions and metabolism of phospholipid and amino acid, transcription, and translation may be implicated in low GZMB expressions. C1QA, C1QB, and GZMB are novel SKCM-relating CTRGs, providing promising immune-related prognostic biomarkers for SKCM.

Systematic Analysis of Tumor Microenvironment Patterns and Oxidative Stress Characteristics of Endometrial Carcinoma Mediated by 5-Methylcytosine Regulators

As a widely distributed RNA methylation modification, m5C is involved in the regulation of tumorigenesis. Nevertheless, its fundamental process is not clear. This research sought to examine the genetic properties of the 5-methylcytosine (m5C) regulator in endometrial carcinoma, as well as the prognostic significance and impact of m5C regulators on oxidative stress. Therefore, the TCGA-UCEC data set was used to explore the characteristics of 17 RNAm5C-related genes in the transcriptome, genome, and regulatory network. The subtypes of RNAm5C in UCEC were identified based on the expression levels of 17 RNAm5C-related genes. The prognosis of RNAm5C-2 was significantly better than that of RNAm5C-1. Then, we examined the differences (variations) across various subtypes in terms of immune cell infiltration (ICI) as well as the expression of immune-related signal markers. The findings demonstrated that there were distinct variations in the infiltration level of immune cells in each subtype, which may be the reason for the differences in the prognosis of each subtype. In addition, the differentially expressed genes (DEGs) among RNAm5C subtypes of different UCEC tumors were identified, and the DEGs significant for survival were screened. After obtaining 34 prognostic genes, the dimensionality was reduced to construct an RNA methylation score (RS). As per the findings, RS is a more accurate marker for determining the prognosis for patients with endometrial cancer. The RS was used to categorize UCEC tumor samples, and these results led to the formation of high-score and low-score groups. The patients in the group with a high-RNA methylation score exhibited a survival time that was considerably longer in contrast with those in the group with a low-RNA methylation score. The capacity of RS to predict whether or not immunotherapy would be beneficial was explored further. In the group with a high-RNA methylation score, the objective response rate to the anti-PD-L1 therapy was substantially greater compared to that observed in the subgroup with a low-RNA methylation score. Additionally, there were variations across various RS groups in terms of clinical features, tumor mutation burden, and the infiltration level of immune cells. After binary tree analysis and PCR verification of 34 prognostic genes, it is finally found that the six genes of MAGOH3P, TRBJ2_3, YTHDF1P1, RP11_323D18.5, RP11_405M12.2, and ADAM30 are significantly overexpressed in cancer tissues. These genes can be used as potential biomarkers of endometrial cancer and provide data support for precise immunotherapy in UCEC tumors.

Identification of RFX5 as prognostic biomarker and associated with immune infiltration in stomach adenocarcinoma

Background

Regulatory factor X (RFX) gene family is a series of encodes transcription factors with a highly conserved DNA binding domain. RFXs played a vital role in the development and progression of cancer. However, the significance of RFXs in stomach adenocarcinoma (STAD) has not been fully clarified.

Methods

Online bioinformatics tools such as GSCALite, Kaplan–Meier Plotter, TIMER, LinkedOmics were used to explore the immunomodulatory function and clinical value of RFXs in STAD.

Results

The mRNA level of RFX1, RFX3, RFX4, RFX5, RFX7 and RFX8 was significantly elevated in STAD tissue versus adjacent normal tissue. We also summarize the copy number variation, single nucleotide variants and drug sensitivity of RFXs in STAD. Prognostic analysis indicated that STAD patients with high RFX5 and RFX7 expression had a better overall survival, first progression, and post-progression survival. Moreover, RFX5 expression was significantly associated with the abundance of immune cells, the expression of immune biomarkers and tumor mutational burden score in STAD. Functional enrichment analysis revealed that RFX5 and its related genes were mainly involved in T cell activation, antigen receptor-mediated signaling pathway, cell adhesion molecules, and Th17 cell differentiation. Validation study further verified the expression and prognosis of RFX5 in STAD. Further univariate and multivariate analyses suggested that pathological stage and RFX5 could be a potential independent prognostic factor for STAD.

Conclusions

RFX5 was a candidate prognostic biomarker and associated with immune infiltration in STAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-022-00794-w.

A Ferroptosis-Related Gene Signature for Predicting Survival and Immunotherapy Effect in Renal Cancer

Background

Most recently, no efficient prognostic indictor is present for kidney cancer. Thus, we aimed to build and validate a new prognostic gene signature for renal cancer patients using the Cancer Genomic Atlas (TCGA).

Methods

A “time-dependent receiver operating characteristic (tROC)” curve was generated, and a log-rank test was performed to assess the performance of the biomarker in training and validation. A “ferroptosis-related gene signature” was developed. In different training and validations sets, tROC and log-rank test were used to validate the biomarker's performance.

Results

In the training set with a P value less than 0.01 and the validation set, the “gene signature” was significantly correlated with survival. Eventually, it was found that the ferroptosis-related gene signature was directly correlated with immune score and the score of tumor mutation, suggesting its role in predicting response to immunotherapy.

Conclusion

We developed and validated a “ferroptosis-related gene signature” that can be sued for patients with kidney cancer. It can also assist in facilitating the plan for treatment and risk stratification.

Machine Learning for Endometrial Cancer Prediction and Prognostication

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

NCKAP1 is a Prognostic Biomarker for Inhibition of Cell Growth in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. Nck-associated protein 1 (NCKAP1) is associated with poor prognosis and tumor progression in several cancer types, but the function and prognostic value of NCKAP1 in ccRCC remain poorly understood. Methods: Using the Ualcan database, we evaluated the correlation between NCKAP1 expression and clinical features of ccRCC. These data were validated by immunohistochemical staining for NCKAP1 in a cohort of ccRCC patients. We assessed the prognostic value of NCKAP1 using GEPIA2 survival analysis. NCKAP1 function was characterized in vitro and in vivo using NCKAP1-overexpression ACHN cell lines. The LinkedOmics and GSCALite databases were used to investigate identify potential NCKAP1-targeted medicines that may play a role in the treatment of ccRCC. The impact of NCKAP1 expression on immune infiltration was also evaluated. Results: NCKAP1 was significantly downregulated in ccRCC and correlated with advanced clinicopathological features and poor prognosis. Overexpression of NCKAP1 in ACHN cells reduced proliferation, invasion and migration capacity in vitro and inhibited tumor growth in vivo. According to the LinkedOmics, GSCALite and TIMER databases, NCKAP1 and related genes function primarily in ribosomal signaling, oxidative phosphorylation, TGF-β, and EMT-related signaling pathways. NCKAP1 was also shown to positively correlate with immune cell types, biomarkers, and immune checkpoints in ccRCCs. Conclusions: NCKAP1 may play a vital tumor-suppressive role in ccRCC and is potentially a useful prognostic biomarker.

Tumor Microenvironment—A Short Review of Cellular and Interaction Diversity

The tumor microenvironment is a complex network of various interactions between immune cells and non-cellular components such as the extracellular matrix, exosomes and interleukins. Moreover, tumor heterogeneity and its constant modification may alter the immunophenotype and become responsible for its resistance regarding the therapies applied However, it should be remembered that in a strongly immunosuppressive neoplastic microenvironment, the immune system cells undergo reprogramming and most often cease to fulfill their original function. Therefore, understanding what happens within the tumor microenvironment, and which mechanisms are responsible for tumor development and progression should let us know how cancer could protect itself against the immune system. The presented review summarizes the latest information on the interactions between the tumor microenvironment and the cellular and non-cellular components, as well as their impact on cancer development, progression and immune system exhaustion.

Prognostic Characteristics of Immune-Related Genes and the Related Regulatory Axis in Patients With Stage N+M0 Breast Cancer

Breast cancer (BRCA) has the highest incidence rate among female tumours. The function of the immune system affects treatment efficacy and prognosis in patients with BRCA. However, the exact role of immune-related genes (IRGs) in stage N+M0 BRCA is unknown. We constructed a predictive risk scoring model with five IRGs (CDH1, FGFR3, INHBA, S100B, and SCG2) based on the clinical, mutation, and RNA sequencing data of individuals with stage N+M0 BRCA sourced from The Cancer Genome Atlas. Results from the Shandong Cancer Hospital and Institute validation cohort suggested that regardless of clinical stage, tumour size, or the number of lymph node metastases, this model was able to reliably discriminate low-risk patients from high-risk ones and assess the prognosis of patients with stage N+M0 BRCA, and low-risk patients could benefit more from immunotherapy than high-risk patients. In addition, significant inter-group variations in immunocyte infiltration and the tumour microenvironment were observed. Moreover, risk score and age were found to be independent factors in multivariate COX regression analysis, which influenced the outcome of patients with stage N+M0 BRCA. Based on the above findings, we plotted a prognostic nomogram. Finally, we constructed a lncRNA KCNQ1OT1-LINC00665-TUG1/miR-9-5p/CDH1 regulatory axis of the ceRNA network to explore the mechanism of BRCA progression. In summary, we conducted a systemic and extensive bioinformatics investigation and established an IRG-based prognostic scoring model. Finally, we constructed a ceRNA regulatory axis that might play a significant role in BRCA development. More research is required to confirm this result. Scoring system-based patient grouping can help predict the outcome of patients with stage N+M0 BRCA more effectively and determine their sensitivity to immunotherapies, which will aid the development of personalised therapeutic strategies and inspire the research and development of novel medications.

New biomarker: the gene HLA-DRA associated with low-grade glioma prognosis

BACKGROUND

Low-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG.

METHODS

Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG.

RESULTS

A total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells.

CONCLUSIONS

HLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.

Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

A Novel Immune-Related Prognostic Signature Predicting Survival in Patients with Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) carries the lowest survival rate of all major organ cancers, which is of dismal prognosis and high mortality rate. Thus, the present study attempted to identify a few novel prognostic biomarkers and establish an immune-related prognostic signature which could predict the prognosis of PAAD. Four prognostic immune-related genes (IRGs) including S100A6, S100A10, S100A16, and SDC1 were screened by differentially expressed gene (DEG) identification and weighted gene coexpression network analysis (WGCNA). Subsequent analysis proved the high expression of these IRGs in PAAD tissues, suggested by TCGA-PAAD data, merged microarray-acquired dataset (MMD), GEPIA, and Oncomine webtool. By using MMD and TCGA-PAAD data, S100A6 (MMD: AUC = 0.897; TCGA: AUC = 0.843), S100A10 (MMD: AUC = 0.880; TCGA: AUC = 0.780), S100A16 (MMD: AUC = 0.878; TCGA: AUC = 0.838), and SDC1 (MMD: AUC = 0.885; TCGA: AUC = 0.812) exhibited excellent diagnostic efficiency for PAAD. By conducting connectivity map (CMap) analysis, we concluded that three molecule drugs (sulpiride, famotidine, and nalidixic acid) might have worked in the treatment of PAAD. Then, an immune-related prognostic index was constructed, which was validated as an independent prognostic factor for PAAD patients (P=0.004). We further constructed a nomogram by using this immune-related signature and age, the prognostic value of which was validated by using concordance index (C-index = 0.780) and area under curve (AUC = 0.909). Moreover, the immune-related prognostic signature was associated with response to anti-PD-1/L1 immunotherapy. To sum up, four IRGs were screened out and verified to be novel immune-related prognostic biomarkers in PAAD. Besides, sulpiride, famotidine, and nalidixic acid might be potential choices in the treatment of PAAD. An immune-related signature was established to show great potential for prognosis prediction for PAAD, independently, which might guide more effective immunotherapy strategies. A nomogram is further established by using this immune-related prognostic index, which might contribute to more effective prognosis prediction in PAAD patients.

Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma

Objective. Malignant melanoma (MM) is one of the most malignant types of skin cancer and its incidence and mortality rates are increasing worldwide. Aging is well recognized as a significant risk factor for cancer. However, few studies have analyzed in depth the association between aging-related genes (AGs) and malignant melanoma prognosis with tumor immune microenvironment. Methods. Here, we downloaded 471 MM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. 58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay. As a result, a gene signature for aging-related genes was created. The time-dependent ROC curve and Kaplan–Meier analysis were calculated to determine its predictive capability. Moreover, we created a nomogram for the clinicopathologic variables and the AGs gene signature to determine overall survival (OS). We also explored the association between three immune checkpoints, immune cell infiltration, and the aging-related gene signature. Results. We established an aging risk model to identify and predict the immune microenvironment in malignant melanoma. Then we developed and validated a prognosis risk model using three AGs (CSNK1E, C1QA, and SOD-2) in the GSE65904 dataset. The aging signature was positively associated with clinical and molecular characteristics and can be used as a prognostic factor for malignant melanoma. The low aging risk score was associated with a poor prognosis and indicated an immunosuppressive microenvironment. Conclusions. To summarize, we established and validated a model of aging risk based on three aging-related genes that acted as an independent prognostic predictor of overall survival. Besides, it also characterized the immune response in the malignant melanoma microenvironment and could provide a potential indicator of individualized immunotherapy in malignant melanoma.

Immune Infiltration in Gastric Cancer Microenvironment and Its Clinical Significance

Immunotherapy has developed rapidly and has gradually become one of the important methods for treatment of gastric cancer (GC). The research on tumor infiltrating immune cells (TIICs) and immune-related genes in the tumor microenvironment (TME) greatly encourages the development of immunotherapy. The devolution algorithm (CIBERSORT) was applied to infer the proportion of 22 TIICs based on gene expression profiles of GC tissues, which were downloaded from TCGA and GEO. TCGA was utilized to analyze the differential expression of immune-related genes, and explore the potential molecular functions of these genes. We have observed the enrichment of multiple TIICs in microenvironment of GC. Some of these cells were closely related to tumor mutational burden (TMB), microsatellite instability (MSI), Fuhrman grade, and TNM staging. Survival analysis showed that the infiltration level of CD8+ T cells, activated CD4+ memory T cells and M2 macrophages were significantly related to the prognosis of GC patients. The functional enrichment analysis of immune-related genes revealed that these genes were mainly associated with cytokine activation and response. Four significant modules were screened by PPI network and 20 key genes were screened from the modules. The expression levels of CALCR and PTH1R are strikingly related to the expression of immune checkpoint and the prognosis of GC patients. The type and number of TIICs in microenvironment of GC, as well as immune-related genes are closely related to tumor progression, and can be used as important indicators for patient prognosis assessment.

Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma

Tumor infiltration, known to associate with various cancer initiations and progressions, is a promising therapeutic target for aggressive cutaneous melanoma. Then, the relative infiltration of 24 kinds of immune cells in melanoma was assessed by a single sample gene set enrichment analysis (ssGSEA) program from a public database. The multiple machine learning algorithms were applied to evaluate the efficiency of immune cells in diagnosing and predicting the prognosis of melanoma. In comparison with the expression of immune cell in tumor and normal control, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC = 0.965, RF AUC = 0.99, SVM AUC = 0.963, LASSO AUC = 0.964, and NNET AUC = 0.989). These diagnostic models were also validated in three outside datasets and suggested over 90% AUC to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker that could estimate the prognosis of melanoma. This biomarker was also further validated in internal and external datasets. Following that, we created a nomogram with a composition of risk score and clinical parameters, which had high accuracies in predicting survival over three and five years. The nomogram's decision curve revealed a bigger net benefit than the tumor stage. Furthermore, a risk score system was used to categorize melanoma patients into high- and low-risk subgroups. The high-risk group has a significantly lower life expectancy than the low-risk subgroup. Finally, we observed that complement, epithelial-mesenchymal transition, and inflammatory response were significantly activated in the high-risk group. Therefore, the findings provide new insights for understanding the tumor infiltration relevant to clinical applications as a diagnostic or prognostic biomarker for melanoma.

Construction and validation of a robust prognostic model based on immune features in sepsis

Purpose

Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.

Materials and methods

The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.

Results

A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).

Conclusions

Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

Landscape and Clinical Significance of Immune Checkpoint in Cutaneous Melanoma

Background

The incidence of cutaneous melanoma (CM) is increasing, and its prognosis is not optimistic. Although immune checkpoint (ICP) inhibitors are effective in the treatment of CM patients, they are not effective for all CM patients. There is an urgent need for a marker to predict both the prognosis and the immunotherapy effect in patients with CM.

Approaches

Two groups of patients with greatly different prognosis and response to immunotherapy were identified by unwatched cluster exploration of TCGA on the basis of 34 ICPs. The prognosis and immunotherapy effect of CM were predicted by developing a precise and given signature on the basis of ICPs, and a multivariate Cox risk regression model was established from the TCGA cohort consisting of 454 CM samples. The model was validated in 210 and 231 samples in the test and verification cohorts, respectively.

Results

The prognosis in clinical subgroups was predicted by the classification system. High-risk patients had poorer responses to chemotherapy and immunotherapy. Finally, the signature was recognized as an independent prognostic factor. Based on checkpoint-based signature (ICPBS) and clinical characteristics, we constructed a nomogram for the prognosis in patients with CM, which was superior to ICPBS in efficacy than ICPBS alone.

Conclusion

As a useful prognostic tool to further improve cancer immunotherapy, the signature can accurately predict recurrence and overall survival among patients with CM.

Identification and Validation of Immune- and Stemness-Related Prognostic Signature of Melanoma

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma. Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma. Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma. Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of hepatocellular carcinoma (HCC)

ABSTRACT

Growing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the "edgeR" package of the R software. Functional annotation and pathway enrichment were performed using "ggplots2" and "clusterProfiler" packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients' prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients' prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.

Chitosan-Poly(Acrylic Acid) Nanoparticles Loaded with R848 and MnCl2 Inhibit Melanoma via Regulating Macrophage Polarization and Dendritic Cell Maturation

Purpose

Since immune cells in the tumor microenvironment (TME) can affect the development and progression of tumors, strategies modulating immune cells are considered to have an important therapeutic effect. As a TLR7/8 agonist, R848 effectively activates the innate immune cells to exert an anti-tumor effect. Mn²⁺ has been reported to strongly promote the maturation of antigen-presenting cells (APCs), thereby enhancing the cytotoxicity of CD8⁺ T cells. Thus, we tried to investigate whether chitosan-poly(acrylic acid) nanoparticles (CS-PAA NPs) loaded with R848 and MnCl₂ (R-M@CS-PAA NPs) could exert an anti-tumor effect by regulating the function of immune cells.

Methods

R-M@CS-PAA NPs were prepared, and their basic characteristics, anti-tumor effect, and potential mechanisms were explored both in vitro and in vivo.

Results

R-M@CS-PAA NPs easily released MnCl₂ and R848 at low pH. In B16F10 mouse melanoma model, R-M@CS-PAA NPs exerted the most significant anti-melanoma effect compared with the control group and CS-PAA NPs loaded with R848 or MnCl₂ alone. FITC-labeled R-M@CS-PAA NPs were displayed to be accumulated at the tumor site. R-M@CS-PAA NPs significantly increased the infiltration of M1 macrophages and CD8⁺ T cells but reduced the number of suppressive immune cells in the TME. Moreover, in vitro experiments showed that R-M@CS-PAA NPs polarized macrophages into the M1 phenotype to inhibit the proliferation of B16F10 cells. R-M@CS-PAA NPs also enhanced the killing function of CD8⁺ T cells to B16F10 cells. Of note, R-M@CS-PAA NPs not only promoted the maturation of APCs such as dendritic cells and macrophages by STING and NF-кB pathways, but also enhanced the ability of dendritic cells to present ovalbumin to OT-I CD8⁺ T cells to enhance the cytotoxicity of OT-I CD8⁺ T cells to ovalbumin-expressing B16F10 cells.

Conclusion

These data indicate that the administration of R-M@CS-PAA NPs is an effective therapeutic strategy against melanoma.

Cullin-5 (CUL5) as a potential prognostic marker in a pan-cancer analysis of human tumors

There is some evidence supporting an association between Cullin-5 (CUL5) and cancer, but no research using pan-cancer analysis has been conducted previously. We therefore investigated the oncogenic role of CUL5 in 33 tumors from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Many cancers reduce CUL5 levels, and the prognosis of certain cancers is vitally linked with CUL5 expression. CUL5 expression is associated with CD8 + T-cell infiltration levels in uveal melanomas and head and neck squamous cell carcinomas, and we observed a positive relationship between CUL5 and Tcm (T central memory) cells, and a negative relationship between T helper (Th) cells and pDC (plasmacytoid DC). CUL5 had negative associations with NK cells, NK CD56^bright cells, NK CD56^dim cells, Tregs, cytotoxic cells, and Th17 cells. Functions relating to protein processing and ubiquitin were included in the CUL5 functional mechanisms. The top 100 genes that are most strongly related to CUL5 were identified, and enrichment analysis indicated that the biological process with the closest relationship was neddylation, related pathways included the TGF-beta signaling pathway and intracellular receptor signaling pathway. CUL5 is related to biological cell behaviors such as chromosome segregation and positive regulation of chromosome organization. As the first study to perform a pan-cancer analysis of CUL5, the present findings will improve the understanding of the oncogenic role of CUL5 in different tumors.

Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer

Background

The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).

Methods

Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.

Results

Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.

Conclusions

In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08629-3.

m6A-Related lncRNA to Develop Prognostic Signature and Predict the Immune Landscape in Bladder Cancer

Abnormal m6A methylation plays a significant role in cancer progression. Increasingly, researchers have focused on developing lncRNA signatures to evaluate the prognosis of cancer patients. The specific function of m6A-related lncRNAs in the prognosis of bladder cancer patients and the immune microenvironment of bladder cancer remains elusive. Herein, we performed a comprehensive analysis of m6A-related lncRNA prognostic values and their association with the immune microenvironment in bladder cancer using the TCGA dataset. A total of 9 m6A-related lncRNAs were dramatically correlated with overall survival outcomes in bladder cancer. Two molecular subtypes (cluster 1 and cluster 2) were identified by consensus clustering for 9 m6A-related prognostic lncRNAs. Cluster 1 was significantly correlated with poor prognosis, advanced clinical stage, higher PD-L1 expression, a higher ESTIMATEScore and immuneScore, and distinct immune cell infiltration. GSEA revealed the enrichment of apoptosis and the JAK-STAT signaling pathway in cluster 2. A prognostic risk score was constructed using 9 m6A-related prognostic lncRNAs, which functioned as an independent prognostic factor for bladder cancer. Moreover, bladder cancer patients in the low-risk score group had a higher pN stage, pT stage, and clinical stage and a lower tumor grade and immuneScore. The risk score was correlated with the infiltration levels of certain immune cells, including B cells, plasma cells, follicular helper T cells, regulatory T cells, resting NK cells, neutrophils, M0 macrophages, M1 macrophages, and M2 macrophages. Collectively, our study elucidated the important role of m6A-related lncRNAs in the prognosis of bladder cancer patients and in the bladder cancer immune microenvironment. The results suggest that the components of the m6A-related prognostic lncRNA signature might serve as a crucial mediator of the immune microenvironment in bladder cancer, representing promising therapeutic targets for improving immunotherapeutic efficacy.

Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma

We aimed to fully understand the landscape of the skin cutaneous melanoma (SKCM) microenvironment and develop an immune prognostic signature that can predict the prognosis for SKCM patients. RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The immune-prognostic signature was constructed by LASSO Cox regression analysis. We calculated the relative abundance of 29 immune-related gene sets based on the mRNA expression profiles of 314 SKCM patients in the Cancer Genome Atlas training set. Hierarchical clustering was performed to classify SKCM patients into three clusters: immunity-high, -medium and -low. The values of our prognostic model in predicting disease progression, metastasis and immunotherapeutic responses were also validated. In conclusion, the prognostic model demonstrated a powerful ability to distinguish and predict SKCM patients' prognosis.

Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

Background

Pancreatic adenocarcinoma (PAAD) is a highly lethal and aggressive tumor with poor prognoses. The predictive capability of immune-related genes (IRGs) in PAAD has yet to be explored. We aimed to explore prognostic-related immune genes and develop a prediction model for indicating prognosis in PAAD.

Methods

The messenger (m)RNA expression profiles acquired from public databases were comprehensively integrated and differentially expressed genes were identified. Univariate analysis was utilized to identify IRGs that related to overall survival. Whereafter, a multigene signature in the Cancer Genome Atlas cohort was established based on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Moreover, a transcription factors regulatory network was constructed to reveal potential molecular processes in PAAD. PAAD datasets downloaded from the Gene Expression Omnibus database were applied for the validations. Finally, correlation analysis between the prognostic model and immunocyte infiltration was investigated.

Results

Totally, 446 differentially expressed immune-related genes were screened in PAAD tissues and normal tissues, of which 43 IRGs were significantly related to the overall survival of PAAD patients. An immune-based prognostic model was developed, which contained eight IRGs. Univariate and multivariate Cox regression revealed that the risk score model was an independent prognostic indicator in PAAD (HR > 1, P < 0.001). Besides, the sensitivity of the model was evaluated by the receiver operating characteristic curve analysis. Finally, immunocyte infiltration analysis revealed that the eight-gene signature possibly played a pivotal role in the status of the PAAD immune microenvironment.

Conclusion

A novel prognostic model based on immune genes may serve to characterize the immune microenvironment and provide a basis for PAAD immunotherapy.

Tumor-Penetrable Nitric Oxide-Releasing Nanoparticles Potentiate Local Antimelanoma Therapy

Although nitric oxide (NO) has been emerging as a novel local anticancer agent because of its potent cytotoxic effects and lack of off-target side effects, its clinical applications remain a challenge because of the short effective diffusion distance of NO that limits its anticancer activity. In this study, we synthesized albumin-coated poly(lactic-co-glycolic acid) (PLGA)-conjugated linear polyethylenimine diazeniumdiolate (LP/NO) nanoparticles (Alb-PLP/NO NPs) that possess tumor-penetrating and NO-releasing properties for an effective local treatment of melanoma. Sufficient NO-loading and prolonged NO-releasing characteristics of Alb-PLP/NO NPs were acquired through PLGA-conjugated LP/NO copolymer (PLP/NO) synthesis, followed by nanoparticle fabrication. In addition, tumor penetration ability was rendered by the electrostatic adsorption of the albumin on the surface of the nanoparticles. The Alb-PLP/NO NPs showed enhanced intracellular NO delivery efficiency and cytotoxicity to B16F10 murine melanoma cells. In B16F10-tumor-bearing mice, the Alb-PLP/NO NPs showed improved extracellular matrix penetration and spatial distribution in the tumor tissue after intratumoral injection, resulting in enhanced antitumor activity. Taken together, the results suggest that Alb-PLP/NO NPs represent a promising new modality for the local treatment of melanoma.

Toward precision immunotherapy using multiplex immunohistochemistry and in silico methods to define the tumor immune microenvironment

Recent developments in cancer immunotherapy promise better outcomes for cancer patients, although clinical trials for difficult to treat cancers such as malignant brain cancer present special challenges, showing little response to first generation immunotherapies. Reasons for differences in immunotherapy response in some cancer types are likely due to the nature of tumor microenvironment, which harbors multiple cell types which interact with tumor cells to establish immunosuppression. The cell types which appear to hold the key in regulating tumor immunosuppression are the tumor-infiltrating immune cells. The current standard treatment for difficult to treat cancer, including the most malignant brain cancer, glioblastoma, continues to offer a bleak outlook for patients. Immune-profiling and correlation with pathological and clinical data will lead to a deeper understanding of the tumor immune microenvironment and contribute toward the selection, optimization and development of novel precision immunotherapies. Here, we review the current understanding of the tumor microenvironmental landscape in glioblastoma with a focus on next-generation technologies including multiplex immunofluorescence and computational approaches to map the brain tumor microenvironment to decipher the role of the immune system in this lethal malignancy.

A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma

Tumor microenvironment (TME) has been reported to exhibit a crucial effect in lung cancer. Therefore, this study was aimed to investigate the genes associated with TME and develop a risk score to predict the overall survival (OS) of patients with lung adenocarcinoma (LUAD) based on these genes. The immune and stromal scores were generated by the ESTIMATE algorithm for LUAD patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed gene and weighted gene co-expression network analyses were used to derive immune- and stromal-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied for further selection and the selected genes were inputted into stepwise regression to develop TME-related risk score (TMErisk) which was further validated in Gene Expression Omnibus (GEO) datasets. TMErisk-related biological phenotypes were analyzed in function enrichment, tumor immune signature, and tumor mutation signature. The patient's response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). According to our results, TMErisk was developed based on SERPINE1, CX3CR1, CD200R1, GBP1, IRF1, STAP1, LOX, and OR7E47P. Furthermore, high TMErisk was identified as a poor factor for OS in TCGA and GEO datasets, as well as in subgroup analysis with different gender, smoking status, age, race, anatomic site, therapies, and tumor-node-metastasis (TNM) stages. Higher TMErisk is also associated negatively with the abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and other stromal or immune cells. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were less expressed in the high-TMErisk group. Mutations of 19 genes occurred more frequently in the high-TMErisk group. These mutations may be associated with TME change and indicate patients' response to immunotherapy. According to our analyses, a lower TMErisk score may indicate better response and OS outcome of immunotherapy.

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

CD2 Is a Novel Immune-Related Prognostic Biomarker of Invasive Breast Carcinoma That Modulates the Tumor Microenvironment

Female breast cancer (BCa) is the most commonly occurring cancer worldwide. The tumor microenvironment (TME) plays an essential role in tumor invasion, angiogenesis, unlimited proliferation, and even immune escape, but we know little about the TME of BCa. In this study, we aimed to find a TME-related biomarker for BCa, especially for invasive breast carcinoma (BRCA), that could predict prognosis and immunotherapy efficacy. Based on RNA-seq transcriptome data and the clinical characteristics of 1222 samples (113 normal and 1109 tumor samples) from The Cancer Genome Atlas (TCGA) database, we used the ESTIMATE algorithm to calculate the ImmuneScore and StromalScore and then identified differentially expressed genes (DEGs) between the high and low ImmuneScore groups and the high and low StromalScore groups. Thereafter, a protein–protein interaction (PPI) network analysis and univariate Cox regression analyses of overall survival were used to identify potential key genes. Five candidate genes were identified, comprising CD2, CCL19, CD52, CD3E, and ITK. Thereafter, we focused on CD2, analyzing CD2 expression and its association with survival. CD2 expression was associated with tumor size (T stage) to some extent, but not with overall TNM stage, lymph node status (N stage), or distant metastasis (M stage). High CD2 expression was associated with longer survival. METABRIC data were used to validate the survival result (n = 276). Gene set enrichment analysis (GSEA) showed that the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly associated with high CD2 expression were mainly immune-related pathways. Furthermore, CD2 expression was correlated with 16 types of tumor-infiltrating immune cells (TICs). Hence, CD2 might be a novel biomarker in terms of molecular typing, and it may serve as a complementary approach to TNM staging to improve clinical outcome prediction for BCa patients.

A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Background

Currently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.

Methods

Four hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.

Results

Four genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.

Conclusion

We developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

Single-cell analysis reveals metastatic cell heterogeneity in clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is one of the leading causes of cancer-related death worldwide. Tumour metastasis and heterogeneity lead to poor survival outcomes and drug resistance in patients with metastatic RCC (mRCC). In this study, we aimed to assess intratumoural heterogeneity (ITH) in mRCC cells by performing a combined analysis of bulk data and single-cell RNA-sequencing data, and develop novel biomarkers for prognosis prediction on the basis of the potential molecular mechanisms underlying tumorigenesis. Eligible single-cell cohorts related to mRCC were acquired using the Gene Expression Omnibus (GEO) dataset to identify potential mRCC subpopulations. We then performed gene set variation analysis to understand the differential function in primary RCC and mRCC samples. Subsequently, we applied weighted correlation network analysis to identify coexpressing gene modules that were related to the external trait of metastasis. Protein-protein interactions were used to screen hub subpopulation-difference (sub-dif) markers (ACTG1, IL6, CASP3, ACTB and RAP1B) that might be involved in the regulation of RCC metastasis and progression. Cox regression analysis revealed that ACTG1 was a protective factor (HR < 1), whereas the other four genes (IL6, CASP3, ACTB and RAP1B) were risk factors (HR > 1). Kaplan-Meier survival analysis suggested the potential prognostic value of these sub-dif markers. The expression of sub-dif markers in mRCC was further evaluated in clinical samples by immunohistochemistry (IHC). Additionally, the genetic features of sub-dif marker expression patterns, such as genetic variation profiles, correlations with tumour-infiltrating lymphocytes (TILs), and targeted signalling pathway activities, were assessed in bulk RNA-seq datasets. In conclusion, we established novel subpopulation markers as key prognostic factors affecting EMT-related signalling pathway activation in mRCC, which could facilitate the implementation of a treatment for mRCC patients.

Analysis of the PD-1 Ligands Among Gastrointestinal Cancer Patients: Focus on Cancer Immunity

Many types of gastrointestinal cancer have shown promising outcomes after checkpoint blockade immunotherapy; however, it remains largely unclear about the expression profiles of programmed death 1 (PD-1) ligands (CD274 and PDCD1LG2) in the context of human pan-cancer. This work comprehensively analyzed the expression pattern of the PD-1 ligands and the clinical significance in the prognosis prediction among the seven types of gastrointestinal malignancies collected from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) database. Furthermore, the correlation of CD274/PDCD1LG2 with cancer immunity was also explored. The patients with liver hepatocellular carcinoma (LIHC) receiving cytokine-induced killer (CIK) cell immunotherapy at our cancer center were enrolled. CD274 and PDCD1LG2 displayed inconsistent gene expression levels among the diverse cancer cell lines. Typically, the abnormal expression level of CD274 and PDCD1LG2 was detected in both esophageal carcinoma (ESCA) and stomach adenocarcinoma (STAD), where PDCD1LG2 was related to the overall survival (OS) of the patients in ESCA (p = 0.015) and STAD (p = 0.025). High-serum CD274 and PDCD1LG2 levels predicted a worse survival in the patients with LIHC receiving CIK therapy. More importantly, the expression level of CD274 and PDCD1LG2 was significantly correlated with the degree of Estimation of STromal and Immune cells in MAlignant Tumor tissues using the Expression data (ESTIMATE). In addition, we found that CD274 and PDCD1LG2 were correlated with gene markers in tumor-infiltrating immune cells. Furthermore, the expression of CD274 and PDCD1LG2 was correlated with tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) of different types of cancers. The present work comprehensively analyzed a RNA sequencing of the PD-1 ligands across the seven distinct types of gastrointestinal cancers, which provided clues for further studies in cancer immunity and development.

Single-cell melanoma transcriptomes depicting functional versatility and clinical implications of STIM1 in the tumor microenvironment

Rationale: Previous studies have implicated the functions of stromal interaction molecule 1 (STIM1) in immunity and malignancy, however, the specificity and effects of STIM1 expression in malignant and non-malignant cells in the tumor microenvironment are unclear.

Methods: In the current study, we posed two central questions: (1) does STIM1 expression elicit different cellular programs in cell types within the melanoma tumor microenvironment (2) whether the expression of STIM1 and STIM1-coexpressed genes (SCGs) serve as prognostic indicators of patient's outcomes? To answer these questions, we dissected cell-specific STIM1-associated cellular programs in diverse cell types within the melanoma tumor microenvironment by measuring cell-type specificity of STIM1 expression and SCGs.

Results: A distinct set of SCGs was highly affected in malignant melanoma cells, but not in the other cell types, suggesting the existence of malignant-cell-specific cellular programs reflected by STIM1 expression. In contrast to malignant cells, STIM1 expression appeared to trigger universal and non-specific biological functions in non-malignant cell types, as exemplified by the transcriptomes of macrophages and CD4⁺ T regulatory cells. Results from bioinformatic analyses indicated that SCGs in malignant cells may alter cell-cell interactions through cytokine/chemokine signaling and/or orchestrate immune infiltration into the tumor. Moreover, a prognostic association between SCGs in CD4⁺ T regulatory cells and patient's outcomes was identified. However, we didn't find any correlation between SCGs and responsiveness of immunotherapy.

Conclusions: Overall, our results provide an integrated biological framework for understanding the functional and clinical consequences of cell-specific STIM1 expression in melanoma.

Identification of prognostic biomarkers associated with metastasis and immune infiltration in osteosarcoma

Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.

Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of colorectal cancer

In this study, we used the ESTIMATE algorithm to analyze clinical data and transcriptome profiles of 1635 colorectal cancer (CRC) samples from the Gene Expression Omnibus and The Cancer Genome Atlas databases and identify prognostic immune-related genes (IRGs). We identified 941 differentially expressed (4 downregulated and 937 upregulated) genes by comparing samples with high and low immune, stromal scores and tumor purity. LASSO Cox regression analyses showed that the risk score based on a ten-IRG signature was an independent prognostic factor in CRC. The nomogram with pathological stages (TNM) and the ten-IRG signature showed a C-index of 0.769 (95% CI, 0.717-0.821), and area under ROC curve values of 0.788, 0.782 and 0.789 for 1-, 3-, and 5-year OS, respectively. TIMER database analysis showed positive correlation between the ten prognostic IRGs and the levels of tumor-infiltrated immune cells, including CD4⁺ and CD8⁺ T cells, macrophages, neutrophils, and dendritic cells. These findings demonstrate that this novel ten-IRG signature correlates with the pathological stages and the levels of multiple tumor-infiltrated immune cell types. This makes the ten-IRG signature a potential prognostic factor for CRC patients.